fNIRS Parkinson resting-state study - manuscript
Introduction
Text
Material and methods
Hypotheses
Introduce hypotheses here? H1-H3.
Participants
People with Parkinson’s disease (≥ 60 years, clinical diagnosis ≥ 6 months prior to enrollment) and age- and sex-matched controls were re-invited from the Park-MOVE cohort (Franzén 2023), who had previously participated in an fNIRS study on complex walking. Exclusion criteria for the control group were medical conditions affecting gait and balance, or severe hearing or visual impairment. Exclusion criteria for the Parkinson group included other neurological diseases, severe hearing or visual impairment, other medical conditions affecting gait and balance, and speech difficulties such as aphasia.
Experimental procedure
Data collection took place at the uMOVE core facility, Karolinska University Hospital, Solna, Stockholm. All data was collected during a single experimental session. Collected data consisted of resting-state fNIRS data, cognitive screening data (MoCA), and a clinical test of balance. The Parkinson group also performed the Movement Disorder Society sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) to assess disease severity at the time of measurement.
Data acquisition
The fNIRS system used was a NIRSport2 (NIRx) with 16 sources and 15 detectors, with 8 short-separation detectors. Optodes transmitted light at 760 and 850 nm. Sampling frequency was 7.6 Hz. Because optodes were not sufficient to ensure full-brain coverage, two measurements were taken in succession with one montage for the left hemisphere, and one for the right hemisphere (Figure 1). Each resting-state measurement lasted for 10 minutes.
The resting-state measurements took place in a calm, dimly lit room, with participants seated in an office chair, legs placed on a leg rest to decrease movement. Eyes were closed, and participants were blindfolded as well. Ear plugs were used to shield from ambient noise. Participants were instructed not to focus on any special thought, let the mind wander, and to not fall asleep.
Data preprocessing
Preprocessing of resting-state fNIRS data was performed with the MATLAB NIRS AnalyzIR toolbox (forked version; see Data availability for details) (Santosa et al. 2018). Raw data was trimmed at the start and end of the measurement with 10 seconds on each side to avoid noise from position adjustments and similar. Raw data was then converted into optical density and into ΔHbO2 and ΔHHb using the modified Beer-Lambert law (Delpy et al. 1988). The differential path-length factor (DPF) was set to depend on age (Scholkmann et al. 2013).
Data quality was assessed using MNE-NIRS (v0.7.1) (Gramfort et al. 2013; Luke et al. 2021). The scalp coupling index (SCI) was calculated on a per-channel basis and the percentage of bad channels (SCI < 0.7) among participants was plotted on the montage (supplementary). The worst quality channels were discarded from further analysis, 6 in total, leaving 44 long channels remaining. Moreover, because noise in short-channels may transfer noise into the signal of interest during short-channel regression, quality of the short-channel data was further assessed using their power spectrums (Novi et al. 2023) and channels were discarded if they lacked a clear heart-rate peak around 1 Hz (details of discarded channels in supplementary material).
Data analysis
All-to-all channel correlation matrices were calculated from ΔHbO2 values using the connectivity module in the NIRS toolbox, with prewhitening and short-channel regression (Santosa et al. 2017; Lanka, Bortfeld, and Huppert 2022).
To obtain graph metrics, in particular eigenvector centrality, correlation matrices were fed into the BRAPH2.0 (v2.0.0.b2) (Mijalkov et al. 2017) pipeline Connectivity Comparison WU (weighted undirected). This pipeline compares two groups of subjects by constructing weighted undirected graphs from input connectivity data, in this case the correlation matrices. Brain atlases for the BRAPH2.0 pipeline were obtained by exporting the projected MNI coordinates of channels in the used montages via AtlasViewer (v2.44.0) (Aasted et al. 2015).
Differences in eigenvector centrality values were compared by non-parametric permutation tests with 10000 permutations, considered significant for a two-tailed t-test at p < .05. To account for multiple comparisons in the network results, false discovery rate (FDR) correction was applied, and the network was tested at q < .05.
Finally, to test hypotheses in H3, correlation between eigenvector centrality and disease severity (MDS-UPDRS motor score), levodopa equivalent daily dose (LEDD), disease duration and MoCA score were calculated. Correlations were calculated for those channels showing the largest differences in eigenvector centrality values between the groups.
Results
Show the code
# Load and prepare all data
braph_left_group <- "../data/braph_left_group_results.csv"
braph_left_subject <- "../data/braph_left_subject_results.csv"
braph_right_group <- "../data/braph_right_group_results.csv"
braph_right_subject <- "../data/braph_right_subject_results.csv"
redcap_file <- "../data/REDcap_data.csv"
updrs_file <- "../data/REDcap_data_updrs.csv"
# This file contains data from the 1st measurement, including diagnosis date
redcap_file_parkmove <- "../../Park-MOVE_fnirs_dataset_v2/REDcap_data/All_REDcap_data.csv"
demo_file_parkmove <- "../../Park-MOVE_fnirs_dataset_v2/basic_demographics.csv"
# This file has measurement date
measurement_date_file <- "../data/measurement_dates_rs_fnirs.csv"
# How handle correlations?
corr_method <- "spearman"
# Load the data files
braph_left_group_data <- read.csv(braph_left_group)
braph_left_subject_data <- read.csv(braph_left_subject)
braph_right_group_data <- read.csv(braph_right_group)
braph_right_subject_data <- read.csv(braph_right_subject)
redcap_data <- read.csv(redcap_file)
redcap_data <- redcap_data[c("id_nummer", "moca_sum", "moca_sum_adjusted", "mb_total", "led_total", "frandin_grimby", "ramlat_12_man", "antal_ggr_ramlat_12_man")]
redcap_data['ramlat_12_man'][redcap_data['ramlat_12_man'] == 0] <- 'No'
redcap_data['ramlat_12_man'][redcap_data['ramlat_12_man'] == 1] <- 'Yes'
redcap_data$ramlat_12_man <- factor(redcap_data$ramlat_12_man, levels=c('No', 'Yes'))
updrs_data <- read.csv(updrs_file)
updrs_data$updrs_3_total <- rowSums(updrs_data[, 35:66], na.rm = TRUE)
updrs_data <- updrs_data[c("id_nummer", "updrs_3_total", "mdsupdrs3_hy")]
# Merge REDcap demographics data and UPDRS data
all_subject_data <- merge(redcap_data, updrs_data, by = "id_nummer", all = TRUE)
names(all_subject_data)[names(all_subject_data) == "id_nummer"] <- "subject"
# Make the subject names the same format
all_subject_data <- all_subject_data %>%
mutate(subject = gsub("_rs", "", subject))
# Data from original study for demographics
redcap_data_parkmove <- read.csv(redcap_file_parkmove)
names(redcap_data_parkmove)[names(redcap_data_parkmove) == "id_nummer"] <- "subject"
redcap_data_parkmove <- redcap_data_parkmove[redcap_data_parkmove$subject %in% all_subject_data$subject, ]
demo_data_parkmove <- read.csv(demo_file_parkmove)
demo_data_parkmove <- demo_data_parkmove[demo_data_parkmove$subject %in% all_subject_data$subject, ]
demo_data_parkmove <- demo_data_parkmove[c("subject", "sex", "age", "weight", "height")]
demo_data_parkmove['sex'][demo_data_parkmove['sex'] == 0] <- 'Male'
demo_data_parkmove['sex'][demo_data_parkmove['sex'] == 1] <- 'Female'
demo_data_parkmove$sex <- factor(demo_data_parkmove$sex, levels=c('Male', 'Female'))
# Get disease duration via diagnosis date and measurement date
diagnosis_data <- redcap_data_parkmove[c("subject", "crf_pd_year_phone")]
measurement_dates <- read.csv(measurement_date_file, sep=";")
measurement_dates$measurement_date_t1 <- as.Date(measurement_dates$measurement_date_t1, format = "%Y-%m-%d")
measurement_dates$year <- format(measurement_dates$measurement_date_t1, "%Y")
measurement_dates <- measurement_dates[c("subject", "year")]
measurement_dates <- merge(diagnosis_data, measurement_dates, by = "subject", all = TRUE)
measurement_dates$disease_dur <- as.numeric(measurement_dates$year) - as.numeric(measurement_dates$crf_pd_year_phone)
measurement_dates <- measurement_dates[c("subject", "disease_dur")]
all_subject_data <- merge(all_subject_data, measurement_dates, by = "subject", all = TRUE)
# Add other demographics
all_subject_data <- merge(all_subject_data, demo_data_parkmove, by = "subject", all = TRUE)
# Assign group function
assign_group <- function(df) {
df$group <- case_when(
startsWith(df$subject, "FNP1") ~ "Control",
startsWith(df$subject, "FNP2") ~ "Parkinson",
TRUE ~ NA_character_
)
df$group <- factor(df$group, levels=c('Control', 'Parkinson'))
return(df)
}
all_subject_data <- assign_group(all_subject_data)Demographics
Show the code
# https://cran.r-project.org/web/packages/arsenal/vignettes/tableby.html
# Cleaner names
labels(all_subject_data) <- c(
age = "Age, yrs",
sex = "Gender, female",
weight = "Weight, kg",
height = "Height, cm",
frandin_grimby = "Frändin-Grimby",
ramlat_12_man = "Falls in last 12 months, yes",
mb_total = "Mini-BESTest score")
# Properties for all tables
mycontrols <- tableby.control(numeric.test="kwt", cat.test="chisq", cat.simplify = TRUE, cat.stats="countpct",
numeric.simplify=TRUE, numeric.stats=c("meansd"),
digits=2, digits.p=2, digits.pct=1)
# Tables
tab1 <- tableby(group ~ sex + age + weight + height +
mb_total, data=all_subject_data, control=mycontrols)
summary(tab1, title = "Table 1 - Demographics")| Control (N=30) | Parkinson (N=27) | Total (N=57) | p value | |
|---|---|---|---|---|
| Gender, female | 10 (34.5%) | 10 (37.0%) | 20 (35.7%) | 0.84 |
| Age, yrs | 68.03 (6.74) | 69.63 (6.77) | 68.80 (6.74) | 0.53 |
| Weight, kg | 75.31 (13.98) | 76.81 (19.05) | 76.04 (16.48) | 0.99 |
| Height, cm | 175.24 (10.25) | 174.63 (9.22) | 174.95 (9.68) | 0.79 |
| Mini-BESTest score | 24.47 (2.43) | 19.41 (5.09) | 22.07 (4.64) | < 0.01 |
Eigenvector centrality
The largest eigenvector centrality values were found in the temporal and parietal regions (Figure 2). Values were similar for the left and right hemispheres/sessions and ranged from approximately 0.05 to 0.23 (supplementary table 1 and 2).
Differences in eigenvector centrality values between the groups (Figure 3, supplementary table 1 and 2) revealed a single channel with a significantly lower value in the Parkinson group compared to the control group (SRC10-DET9, HC 0.153, PD 0.119, difference −0.034, p .034). The center of this channel was according to AtlasViewer simulations located at MNI coordinates (-39 -54 46). However, the channel did not survive FDR-adjustment.
